classification.C

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/// \file
/// \ingroup tutorial_tmva_envelope
/// \notebook -nodraw
 
/// \macro_code
/// \macro_output
 
 
 
#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/Tools.h"
#include "TROOT.h"
#include "TMVA/Classification.h"
 
void classification(UInt_t jobs = 4)
{
   TMVA::Tools::Instance();
 
   TFile *input(nullptr);
   TString fname = gROOT->GetTutorialDir() + "/machine_learning/data/tmva_class_example.root";
   if (!gSystem->AccessPathName(fname)) {
      input = TFile::Open(fname); // check if file in local directory exists
   }
   if (!input) {
      std::cout << "ERROR: could not open data file" << fname << std::endl;
      exit(1);
   }
 
   // Register the training and test trees
 
   TTree *signalTree = (TTree *)input->Get("TreeS");
   TTree *background = (TTree *)input->Get("TreeB");
 
   TMVA::DataLoader *dataloader = new TMVA::DataLoader("dataset");
   // If you wish to modify default settings
   // (please check "src/Config.h" to see all available global options)
   //
   //    (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
   //    (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";
 
   // Define the input variables that shall be used for the MVA training
   // note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
   // [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
   dataloader->AddVariable("myvar1 := var1+var2", 'F');
   dataloader->AddVariable("myvar2 := var1-var2", "Expression 2", "", 'F');
   dataloader->AddVariable("var3", "Variable 3", "units", 'F');
   dataloader->AddVariable("var4", "Variable 4", "units", 'F');
 
   // You can add so-called "Spectator variables", which are not used in the MVA training,
   // but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
   // input variables, the response values of all trained MVAs, and the spectator variables
 
   dataloader->AddSpectator("spec1 := var1*2", "Spectator 1", "units", 'F');
   dataloader->AddSpectator("spec2 := var1*3", "Spectator 2", "units", 'F');
 
   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight = 1.0;
   Double_t backgroundWeight = 1.0;
 
   // You can add an arbitrary number of signal or background trees
   dataloader->AddSignalTree(signalTree, signalWeight);
   dataloader->AddBackgroundTree(background, backgroundWeight);
 
   // Set individual event weights (the variables must exist in the original TTree)
   // -  for signal    : `dataloader->SetSignalWeightExpression    ("weight1*weight2");`
   // -  for background: `dataloader->SetBackgroundWeightExpression("weight1*weight2");`
   dataloader->SetBackgroundWeightExpression("weight");
   dataloader->PrepareTrainingAndTestTree(
      "", "", "nTrain_Signal=1000:nTrain_Background=1000:SplitMode=Random:NormMode=NumEvents:!V");
 
   TFile *outputFile = TFile::Open("TMVAClass.root", "RECREATE");
 
   TMVA::Experimental::Classification *cl = new TMVA::Experimental::Classification(dataloader, Form("Jobs=%d", jobs));
 
   cl->BookMethod(TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=2000:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:"
                                             "UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2");
   cl->BookMethod(TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm");
 
   cl->BookMethod(TMVA::Types::kBDT, "BDTB", "!H:!V:NTrees=2000:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20");
 
   cl->BookMethod(TMVA::Types::kCuts, "Cuts", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart");
 
   cl->Evaluate(); // Train and Test all methods
 
   auto &results = cl->GetResults();
 
   TCanvas *c = new TCanvas(Form("ROC"));
   c->SetTitle("ROC-Integral Curve");
 
   auto mg = new TMultiGraph();
   for (UInt_t i = 0; i < results.size(); i++) {
      if (!results[i].IsCutsMethod()) {
         auto roc = results[i].GetROCGraph();
         roc->SetLineColorAlpha(i + 1, 0.1);
         mg->Add(roc);
      }
   }
   mg->Draw("AL");
   mg->GetXaxis()->SetTitle(" Signal Efficiency ");
   mg->GetYaxis()->SetTitle(" Background Rejection ");
   c->BuildLegend(0.15, 0.15, 0.3, 0.3);
   c->Draw();
 
   outputFile->Close();
   delete cl;
}